Schedule

The session grid for the whole course: what each session covers, the materials it uses, and the dates that matter — the two written tests, the project milestones and the two seminars.

Tentative schedule

Dates are provisional until the official timetable is published. Times and rooms will be added here.

Module 1 — Statistical Data Analysis

#	Date		Topic and materials
1	Thu 1 Oct	Lecture	Course introduction · A tour of Python for data analysis What the course is about, how the exam and the project work, and a guided tour of Python and the data-science stack. The three notebooks of the Python self-study are assigned here. 📌 Python self-study assigned · project form opens
2	Tue 6 Oct	Lecture	Key concepts of data analysis notes · slides
3	Thu 8 Oct	Lecture	Describing and visualizing the data notes · slides 📌 Project form (MS Forms) due Sun 11 October — group and preferences
4	Tue 13 Oct	Lecture	Probability for data analysis notes · slides 📌 Datasets assigned by e-mail · fork the project template and start Part 1
5	Thu 15 Oct	Lecture	Association between variables notes · slides
6	Tue 20 Oct	Lecture	Data distributions notes · slides
7	Thu 22 Oct	Lecture	Statistical inference: sampling, confidence intervals, bootstrap, hypothesis testing notes and slides: TBA
8	Tue 27 Oct	Lecture	Statistical tests in practice: comparing groups, effect size, multiple tests notes and slides: TBA
9	Thu 29 Oct	Lecture	Linear regression notes and slides: TBA
10	Tue 3 Nov	Lecture	Logistic regression notes and slides: TBA
11	Thu 5 Nov	Lecture	Extending regression models: interactions, polynomial terms, multinomial regression notes and slides: TBA
12	Tue 10 Nov	Lecture	Causal analysis and experimental design notes and slides: TBA
13	Thu 12 Nov	Lecture	Storytelling with data notes and slides: TBA The deck of this lesson is shared on Teams: it reproduces figures that cannot be redistributed.
14	Tue 17 Nov	Assessment	Prova in itinere 1 · Project Part 1 review 15 questions in 30 minutes on chapters 1–12. The rest of the session is spent on Part 1 of the project: an informal review at the desks, with the teacher and the tutor going round. Part 1 does not have to be finished. 📌 Part 1 reviewed in class

Module 2 — Predictive Analysis and Data Representation

#	Date		Topic and materials
15	Thu 19 Nov	Lecture	Introduction to predictive analysis: empirical risk, baselines, splits, cross-validation notes and slides: TBA
16	Tue 24 Nov	Lecture	Regression for prediction: overfitting, regularization, the scikit-learn workflow notes and slides: TBA
17	Thu 26 Nov	Lecture	Classification: metrics, KNN, logistic regression as a predictor notes and slides: TBA
18	Tue 1 Dec	Lecture	Generative classifiers: QDA, LDA, naive Bayes notes and slides: TBA
19	Thu 3 Dec	Lecture	Data representation and clustering notes and slides: TBA Feature spaces and distances first, then K-means and how to choose K.
20	Thu 10 Dec	Lecture	Density estimation notes and slides: TBA
21	Tue 15 Dec	Lecture	Dimensionality reduction: principal component analysis notes and slides: TBA 📌 Brief 2 walked through in class — you can work on it over the break
22	Thu 7 Jan	Seminar	Seminar 1 TBD Guest lecture — for example, an introduction to deep learning.
23	Tue 12 Jan	Seminar	Seminar 2 TBD Guest lecture from research or industry.
24	Thu 14 Jan	Assessment	Prova in itinere 2 · Project Q&A 15 questions in 30 minutes on chapters 13–20. The rest of the session is open for questions on the project. 📌 Part 2 due Sun 31 January · presentations in early February